Design a low-latency RAG system

Q: Design a low-latency RAG system

This question evaluates production ML system design competencies, specifically retrieval-augmented generation (RAG) architecture, latency and cost optimization, indexing and retrieval strategies, caching, re-ranking, safety and operational reliability, and evaluation metrics.

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Question

System Design: Production-Grade RAG for Customer Support (p99 ≤ 1.5 s)

Goal

Design a production-ready retrieval-augmented generation (RAG) system for a customer-support assistant with strict latency (target p99 ≤ 1.5 s) and cost constraints.

Requirements

End-to-end architecture:
- Document ingestion
- Chunking strategy
- Embedding model choice
- Index type (vector, BM25, or hybrid)
- Caching layers
- Re-ranking
- Prompt orchestration
- Safety/guardrail components
Data management and reliability:
- Handling document updates and deletions
- Multi-tenant data isolation
- Failure modes (empty retrieval, timeouts, stale cache) and fallbacks
Evaluation:
- Offline and online metrics (answer accuracy, hallucination rate, latency, cost)
- Experiment plan (e.g., BM25 + cross-encoder re-ranker vs dense-only)
- A/B test outline
Scaling plan and capacity estimates for 10 million documents and 50 QPS:
- Index sizing, throughput, and cost
Prioritization under time pressure:
- What to cover vs. de-scope first in a 15-minute interview presentation

Design a low-latency RAG system

System Design: Production-Grade RAG for Customer Support (p99 ≤ 1.5 s)

Goal

Requirements

Solution

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Design a low-latency RAG system

Overview

System Design: Production-Grade RAG for Customer Support (p99 ≤ 1.5 s)

Goal

Requirements

Solution

Comments (0)